Researchers routinely choose an alpha level of 0.05 for testing their hypotheses. What are some experiments for which you might want a lower alpha level (e.g., 0.01)? What are some situations in which you might accept a higher level (e.g., 0.1)?

ADDITIONAL DETAILS 

The alpha level of 0.05 for testing hypotheses

Introduction

The alpha level is the probability of rejecting the null hypothesis when that hypothesis is true. Basically, it’s a number that represents how likely you are to reject the null hypothesis when it’s true. For example, if we set our alpha level to 0.05 then for each test we conduct, we’ll find an effect 5% of the time even though there is no real effect present (and in fact there might be an effect but not in our data).

Section: The alpha level for a statistical test is the probability of finding an effect, given that there is no real effect present.

The alpha level for a statistical test is the probability of finding an effect, given that there is no real effect present. The alpha level can be set to any value between 0 and 1. The larger this value, the more conservative your conclusions will be (i.e., you’ll report less of an effect).

The reason we use such large values here is because they allow us to reject hypotheses even when there are no significant differences between groups—a situation where everyone would expect there to be differences but then fail to find them in reality due to chance alone!

Section: In order to understand the alpha level, we need to first understand that a statistical test will give us values such as t-values and p-values.

The alpha level is the probability of finding an effect, given that there is no real effect present.

The alpha level defines the tendency of a statistical test to reject the null hypothesis when that hypothesis is true. In other words, it tells us how likely it is that our data would show an effect if there were one.

Section: Both t-value and p-values are measures of how “extreme” an observed difference is.

Both t-value and p-value are measures of how “extreme” an observed difference is. The t-value tells you how far apart two means are and the p value tells you whether we can reject the null hypothesis (belief).

For example, if our sample size is 20 people with a mean of $100 and a standard deviation of 5 dollars, then:

• The (two sided) z-score would be 1.0 – this means that there is no difference between their means (they’re equal). So:

• The p value = 0.05 = 5% chance to get this result due to chance alone…so we can’t reject H0

Section: If we set our alpha level to 0.05, then for each test we conduct, we’ll find an effect 5% of the time even though there is no real effect present.

The alpha level is the probability of finding a difference, given that there is no real effect present. The idea behind this concept is that if you set your alpha level at 0.05 (which means that for each test you conduct, we find an effect 5% of the time even though there is no real effect present), then for every test we conduct, our null hypothesis will be rejected half as often as it should be—and thus our data set will contain more false positives than true ones. This difference between actual and expected frequencies can be used to help determine whether or not your statistical test has failed to reject your null hypothesis by chance alone.

The alpha level defines the tendency of a statistical test to reject the null hypothesis when that hypothesis is true.

The alpha level is defined as the probability of rejecting the null hypothesis when it is true. It’s called an “alpha” because it’s the first letter in alphabetically sorted order after H0, which means that this value should be close to 0.05 for most experiments.

For example, if you’re testing whether your product can make people feel better or worse after eating it (the null hypothesis), then an alpha value of 0.005 means there’s five times more evidence against than supporting your theory—and thus, you should reject this theory with 95% confidence (that is, 95% chance).

Conclusion

The alpha level is a useful tool for researchers to have in their disciplinary toolkit. It’s a way of controlling the degree of certainty you have about your results before proceeding with an experiment or analysis. By setting your alpha level appropriately, you can ensure that you don’t go through unnecessary steps (and therefore waste time) when testing hypotheses about the world around us!